SWOT, Empiricism, and River Modeling
Abstract
Given the insurmountable challenge of measuring all rivers in situ, global river models serve as the foundation of freshwater knowledge past, present, and future. We adopt an inductive empirical framework based on Surface Water and Ocean Topography (SWOT) satellite measurements to assess these models. After controlling for SWOT data quality, we examine 68,347 individual river reaches representing ∼38% of global discharge. We find river models currently struggle in areas of heavy economic development, multi‐channel rivers, arid areas, and many Arctic rivers. After controlling for these expected errors, we find better skill as rivers get wider and that parts of Siberia and China are particularly difficult to model. We also find large variability and spatial heterogeneity to model performance, resisting oversimplification. Our results suggest that leveraging SWOT observations within river models will improve them, but river models must adapt their structure to represent realistic hydraulics to do so.
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BibTeX
@article{gleason2026swot,
title = {{SWOT, Empiricism, and River Modeling}},
author = {Colin J Gleason and Paul D Bates and Michael T Durand and Dongmei Feng and Augusto Getirana and Peirong Lin and Hind Oubanas and Tamlin M Pavelsky and Chaopeng Shen and Laurence C Smith and Jida Wang and Dai Yamazaki and Yuan Yang and Heejin An and Fiona B Bennitt and Stephen J Chuter and Chenqi Fang and Jonathan A Flores and Elisa Friedmann and Sreelekha Jarugula and Haoyu Ji and Theodore Langhorst and Di Long and Dung T Vu and Ziyun Yin and George H Allen and Konstantinos M Andreadis and Sylvain Biancamaria and Casey M Brown and Cédric H David and Ming Pan and Jeffrey C Neal and Stephen P Coss and Merritt E Harlan and Peyman Saemian and Travis T Simmons and Nikki Tebaldi and M J Tourian},
journal = {Geophysical Research Letters},
year = {2026},
month = aug,
doi = {10.1029/2026gl124323},
url = {https://hal.science/hal-05760263v1},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - SWOT, Empiricism, and River Modeling
AU - Colin J Gleason
AU - Paul D Bates
AU - Michael T Durand
AU - Dongmei Feng
AU - Augusto Getirana
AU - Peirong Lin
AU - Hind Oubanas
AU - Tamlin M Pavelsky
AU - Chaopeng Shen
AU - Laurence C Smith
AU - Jida Wang
AU - Dai Yamazaki
AU - Yuan Yang
AU - Heejin An
AU - Fiona B Bennitt
AU - Stephen J Chuter
AU - Chenqi Fang
AU - Jonathan A Flores
AU - Elisa Friedmann
AU - Sreelekha Jarugula
AU - Haoyu Ji
AU - Theodore Langhorst
AU - Di Long
AU - Dung T Vu
AU - Ziyun Yin
AU - George H Allen
AU - Konstantinos M Andreadis
AU - Sylvain Biancamaria
AU - Casey M Brown
AU - Cédric H David
AU - Ming Pan
AU - Jeffrey C Neal
AU - Stephen P Coss
AU - Merritt E Harlan
AU - Peyman Saemian
AU - Travis T Simmons
AU - Nikki Tebaldi
AU - M J Tourian
PY - 2026
DA - 2026/08/24
JO - Geophysical Research Letters
DO - 10.1029/2026gl124323
UR - https://hal.science/hal-05760263v1
AB - Given the insurmountable challenge of measuring all rivers in situ, global river models serve as the foundation of freshwater knowledge past, present, and future. We adopt an inductive empirical framework based on Surface Water and Ocean Topography (SWOT) satellite measurements to assess these models. After controlling for SWOT data quality, we examine 68,347 individual river reaches representing ∼38% of global discharge. We find river models currently struggle in areas of heavy economic development, multi‐channel rivers, arid areas, and many Arctic rivers. After controlling for these expected errors, we find better skill as rivers get wider and that parts of Siberia and China are particularly difficult to model. We also find large variability and spatial heterogeneity to model performance, resisting oversimplification. Our results suggest that leveraging SWOT observations within river models will improve them, but river models must adapt their structure to represent realistic hydraulics to do so.
SN - 0094-8276
SN - 1944-8007
ER -